Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:04, 2.36MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:11<00:00, 5.12KFile/s]
Downloading celeba: 1.44GB [00:29, 49.1MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
/usr/local/lib/python3.5/site-packages/matplotlib/font_manager.py:280: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  'Matplotlib is building the font cache using fc-list. '
Out[2]:
<matplotlib.image.AxesImage at 0x7f2b8e816240>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f2b8e5fecf8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, shape=(None, image_height, image_width, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, shape=(None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, shape=(), name='learning_rate')
    
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    
    kinit = tf.random_normal_initializer(stddev=0.02)
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, kernel_initializer=kinit, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, kernel_initializer=kinit, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, kernel_initializer=kinit, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function 
    alpha = 0.2
    
    kinit = tf.random_normal_initializer(stddev=0.02)
    
    with tf.variable_scope('generator', reuse=(not is_train)):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*256)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x256 now
        
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, kernel_initializer=kinit, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x128 now
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, kernel_initializer=kinit, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 28x28x64 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, kernel_initializer=kinit, padding='same')
        # 28x28x3 now
        
        out = tf.tanh(logits)
        
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=(tf.ones_like(d_model_real) * 0.9)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    print_every = 10
    show_every = 100
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images *= 2.0
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                # Feeding input_real for the Generator to avoid "You must feed a value for placeholder tensor 'input_real' with dtype float"'
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z})

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i + 1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [13]:
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.7344... Generator Loss: 1.3594
Epoch 1/2... Discriminator Loss: 0.6715... Generator Loss: 1.6499
Epoch 1/2... Discriminator Loss: 0.6413... Generator Loss: 1.6581
Epoch 1/2... Discriminator Loss: 0.6603... Generator Loss: 2.3116
Epoch 1/2... Discriminator Loss: 0.5345... Generator Loss: 2.0814
Epoch 1/2... Discriminator Loss: 0.5322... Generator Loss: 2.3733
Epoch 1/2... Discriminator Loss: 0.4967... Generator Loss: 2.5967
Epoch 1/2... Discriminator Loss: 0.4240... Generator Loss: 3.1812
Epoch 1/2... Discriminator Loss: 0.6498... Generator Loss: 3.5986
Epoch 1/2... Discriminator Loss: 2.5112... Generator Loss: 0.2032
Epoch 1/2... Discriminator Loss: 0.5595... Generator Loss: 2.4201
Epoch 1/2... Discriminator Loss: 0.8091... Generator Loss: 1.1096
Epoch 1/2... Discriminator Loss: 0.5966... Generator Loss: 2.1204
Epoch 1/2... Discriminator Loss: 0.6414... Generator Loss: 2.6970
Epoch 1/2... Discriminator Loss: 0.6456... Generator Loss: 1.5791
Epoch 1/2... Discriminator Loss: 0.6161... Generator Loss: 1.9679
Epoch 1/2... Discriminator Loss: 0.9981... Generator Loss: 3.3161
Epoch 1/2... Discriminator Loss: 0.5919... Generator Loss: 2.1145
Epoch 1/2... Discriminator Loss: 0.7578... Generator Loss: 3.0442
Epoch 1/2... Discriminator Loss: 0.6706... Generator Loss: 2.1295
Epoch 1/2... Discriminator Loss: 1.2960... Generator Loss: 3.7240
Epoch 1/2... Discriminator Loss: 0.8220... Generator Loss: 1.1094
Epoch 1/2... Discriminator Loss: 0.6560... Generator Loss: 1.6702
Epoch 1/2... Discriminator Loss: 0.8478... Generator Loss: 2.5485
Epoch 1/2... Discriminator Loss: 0.9000... Generator Loss: 2.6010
Epoch 1/2... Discriminator Loss: 0.6842... Generator Loss: 1.5699
Epoch 1/2... Discriminator Loss: 0.6977... Generator Loss: 2.1395
Epoch 1/2... Discriminator Loss: 0.6373... Generator Loss: 2.0038
Epoch 1/2... Discriminator Loss: 0.7260... Generator Loss: 1.4123
Epoch 1/2... Discriminator Loss: 0.7903... Generator Loss: 1.2050
Epoch 1/2... Discriminator Loss: 0.6868... Generator Loss: 1.5946
Epoch 1/2... Discriminator Loss: 0.9046... Generator Loss: 0.9701
Epoch 1/2... Discriminator Loss: 0.8633... Generator Loss: 1.0214
Epoch 1/2... Discriminator Loss: 1.2472... Generator Loss: 0.5969
Epoch 1/2... Discriminator Loss: 0.6576... Generator Loss: 1.8943
Epoch 1/2... Discriminator Loss: 0.6984... Generator Loss: 1.3414
Epoch 1/2... Discriminator Loss: 0.7249... Generator Loss: 1.4042
Epoch 1/2... Discriminator Loss: 0.7088... Generator Loss: 2.0777
Epoch 1/2... Discriminator Loss: 0.8227... Generator Loss: 1.1019
Epoch 1/2... Discriminator Loss: 0.8054... Generator Loss: 1.0957
Epoch 1/2... Discriminator Loss: 0.7216... Generator Loss: 1.2956
Epoch 1/2... Discriminator Loss: 0.6478... Generator Loss: 1.8728
Epoch 1/2... Discriminator Loss: 0.6085... Generator Loss: 2.1868
Epoch 1/2... Discriminator Loss: 0.7317... Generator Loss: 1.3012
Epoch 1/2... Discriminator Loss: 0.6685... Generator Loss: 1.5125
Epoch 1/2... Discriminator Loss: 0.5957... Generator Loss: 1.8578
Epoch 1/2... Discriminator Loss: 0.6369... Generator Loss: 1.8169
Epoch 1/2... Discriminator Loss: 0.9335... Generator Loss: 0.9877
Epoch 1/2... Discriminator Loss: 0.6120... Generator Loss: 2.1509
Epoch 1/2... Discriminator Loss: 0.6911... Generator Loss: 1.3813
Epoch 1/2... Discriminator Loss: 0.6049... Generator Loss: 1.6616
Epoch 1/2... Discriminator Loss: 0.5583... Generator Loss: 2.2453
Epoch 1/2... Discriminator Loss: 0.5695... Generator Loss: 1.9427
Epoch 1/2... Discriminator Loss: 0.6201... Generator Loss: 1.6627
Epoch 1/2... Discriminator Loss: 0.6441... Generator Loss: 2.2550
Epoch 1/2... Discriminator Loss: 0.6493... Generator Loss: 1.6338
Epoch 1/2... Discriminator Loss: 0.5937... Generator Loss: 2.8125
Epoch 1/2... Discriminator Loss: 0.6498... Generator Loss: 1.6016
Epoch 1/2... Discriminator Loss: 0.6666... Generator Loss: 2.9624
Epoch 1/2... Discriminator Loss: 0.5606... Generator Loss: 1.9754
Epoch 1/2... Discriminator Loss: 0.8218... Generator Loss: 1.1402
Epoch 1/2... Discriminator Loss: 0.7055... Generator Loss: 2.7003
Epoch 1/2... Discriminator Loss: 0.5971... Generator Loss: 2.6478
Epoch 1/2... Discriminator Loss: 0.4909... Generator Loss: 2.4869
Epoch 1/2... Discriminator Loss: 0.5614... Generator Loss: 2.0650
Epoch 1/2... Discriminator Loss: 0.5898... Generator Loss: 2.3726
Epoch 1/2... Discriminator Loss: 0.5900... Generator Loss: 1.8317
Epoch 1/2... Discriminator Loss: 0.6911... Generator Loss: 1.4055
Epoch 1/2... Discriminator Loss: 0.8656... Generator Loss: 1.0941
Epoch 1/2... Discriminator Loss: 0.4955... Generator Loss: 2.3228
Epoch 1/2... Discriminator Loss: 0.5724... Generator Loss: 1.9347
Epoch 1/2... Discriminator Loss: 0.6051... Generator Loss: 2.7590
Epoch 1/2... Discriminator Loss: 0.8381... Generator Loss: 1.1399
Epoch 1/2... Discriminator Loss: 0.6532... Generator Loss: 1.6663
Epoch 1/2... Discriminator Loss: 0.5014... Generator Loss: 2.3861
Epoch 1/2... Discriminator Loss: 0.5441... Generator Loss: 2.5266
Epoch 1/2... Discriminator Loss: 0.5290... Generator Loss: 2.5170
Epoch 1/2... Discriminator Loss: 0.5076... Generator Loss: 2.4242
Epoch 1/2... Discriminator Loss: 0.4609... Generator Loss: 2.6023
Epoch 1/2... Discriminator Loss: 0.5502... Generator Loss: 2.6717
Epoch 1/2... Discriminator Loss: 0.5625... Generator Loss: 1.9695
Epoch 1/2... Discriminator Loss: 0.5796... Generator Loss: 2.4621
Epoch 1/2... Discriminator Loss: 0.4915... Generator Loss: 2.9223
Epoch 1/2... Discriminator Loss: 0.5609... Generator Loss: 2.6446
Epoch 1/2... Discriminator Loss: 0.4834... Generator Loss: 2.4771
Epoch 1/2... Discriminator Loss: 0.6492... Generator Loss: 3.0729
Epoch 1/2... Discriminator Loss: 0.6036... Generator Loss: 2.5931
Epoch 1/2... Discriminator Loss: 0.6096... Generator Loss: 2.4753
Epoch 1/2... Discriminator Loss: 0.5715... Generator Loss: 1.9726
Epoch 1/2... Discriminator Loss: 1.2534... Generator Loss: 0.8003
Epoch 1/2... Discriminator Loss: 0.4860... Generator Loss: 2.4686
Epoch 1/2... Discriminator Loss: 0.5899... Generator Loss: 1.9149
Epoch 1/2... Discriminator Loss: 0.4947... Generator Loss: 2.4106
Epoch 2/2... Discriminator Loss: 0.5759... Generator Loss: 2.0304
Epoch 2/2... Discriminator Loss: 1.2636... Generator Loss: 0.7455
Epoch 2/2... Discriminator Loss: 0.5736... Generator Loss: 2.0161
Epoch 2/2... Discriminator Loss: 0.8419... Generator Loss: 2.8642
Epoch 2/2... Discriminator Loss: 0.7043... Generator Loss: 1.3816
Epoch 2/2... Discriminator Loss: 0.6568... Generator Loss: 2.1352
Epoch 2/2... Discriminator Loss: 0.8108... Generator Loss: 2.6615
Epoch 2/2... Discriminator Loss: 0.6634... Generator Loss: 1.5818
Epoch 2/2... Discriminator Loss: 0.5512... Generator Loss: 2.3443
Epoch 2/2... Discriminator Loss: 0.7568... Generator Loss: 1.4871
Epoch 2/2... Discriminator Loss: 0.5586... Generator Loss: 2.1085
Epoch 2/2... Discriminator Loss: 0.6576... Generator Loss: 2.8950
Epoch 2/2... Discriminator Loss: 0.6188... Generator Loss: 2.6097
Epoch 2/2... Discriminator Loss: 0.8264... Generator Loss: 1.1649
Epoch 2/2... Discriminator Loss: 0.9292... Generator Loss: 0.9438
Epoch 2/2... Discriminator Loss: 0.7833... Generator Loss: 1.1748
Epoch 2/2... Discriminator Loss: 0.9083... Generator Loss: 0.9919
Epoch 2/2... Discriminator Loss: 0.6415... Generator Loss: 2.2234
Epoch 2/2... Discriminator Loss: 0.6065... Generator Loss: 1.7570
Epoch 2/2... Discriminator Loss: 0.6178... Generator Loss: 2.1309
Epoch 2/2... Discriminator Loss: 0.9463... Generator Loss: 0.9226
Epoch 2/2... Discriminator Loss: 0.5771... Generator Loss: 2.3506
Epoch 2/2... Discriminator Loss: 0.6010... Generator Loss: 2.4157
Epoch 2/2... Discriminator Loss: 0.7096... Generator Loss: 1.3787
Epoch 2/2... Discriminator Loss: 0.7772... Generator Loss: 1.1968
Epoch 2/2... Discriminator Loss: 0.6145... Generator Loss: 1.6736
Epoch 2/2... Discriminator Loss: 0.7282... Generator Loss: 1.4500
Epoch 2/2... Discriminator Loss: 1.0640... Generator Loss: 0.9058
Epoch 2/2... Discriminator Loss: 0.5560... Generator Loss: 1.9304
Epoch 2/2... Discriminator Loss: 0.8857... Generator Loss: 0.9864
Epoch 2/2... Discriminator Loss: 0.6445... Generator Loss: 1.5516
Epoch 2/2... Discriminator Loss: 0.5799... Generator Loss: 2.0870
Epoch 2/2... Discriminator Loss: 0.6608... Generator Loss: 2.0387
Epoch 2/2... Discriminator Loss: 0.5795... Generator Loss: 2.1427
Epoch 2/2... Discriminator Loss: 0.6171... Generator Loss: 1.9620
Epoch 2/2... Discriminator Loss: 0.5903... Generator Loss: 1.8385
Epoch 2/2... Discriminator Loss: 0.7922... Generator Loss: 1.2384
Epoch 2/2... Discriminator Loss: 0.9155... Generator Loss: 0.9686
Epoch 2/2... Discriminator Loss: 0.6222... Generator Loss: 1.7654
Epoch 2/2... Discriminator Loss: 0.6990... Generator Loss: 1.5061
Epoch 2/2... Discriminator Loss: 0.6952... Generator Loss: 2.3843
Epoch 2/2... Discriminator Loss: 0.7309... Generator Loss: 1.2653
Epoch 2/2... Discriminator Loss: 0.6145... Generator Loss: 2.3270
Epoch 2/2... Discriminator Loss: 0.7224... Generator Loss: 1.3441
Epoch 2/2... Discriminator Loss: 0.5728... Generator Loss: 2.1051
Epoch 2/2... Discriminator Loss: 0.5670... Generator Loss: 2.6834
Epoch 2/2... Discriminator Loss: 0.6613... Generator Loss: 1.5153
Epoch 2/2... Discriminator Loss: 0.8583... Generator Loss: 1.0251
Epoch 2/2... Discriminator Loss: 0.8703... Generator Loss: 1.0322
Epoch 2/2... Discriminator Loss: 0.7150... Generator Loss: 2.2943
Epoch 2/2... Discriminator Loss: 0.9983... Generator Loss: 0.8536
Epoch 2/2... Discriminator Loss: 0.6596... Generator Loss: 1.8507
Epoch 2/2... Discriminator Loss: 0.5724... Generator Loss: 2.1161
Epoch 2/2... Discriminator Loss: 0.5962... Generator Loss: 1.8808
Epoch 2/2... Discriminator Loss: 0.6147... Generator Loss: 1.7704
Epoch 2/2... Discriminator Loss: 0.5947... Generator Loss: 2.1601
Epoch 2/2... Discriminator Loss: 0.6699... Generator Loss: 1.5375
Epoch 2/2... Discriminator Loss: 0.7059... Generator Loss: 2.3287
Epoch 2/2... Discriminator Loss: 0.5791... Generator Loss: 1.8772
Epoch 2/2... Discriminator Loss: 0.5152... Generator Loss: 2.1841
Epoch 2/2... Discriminator Loss: 0.5669... Generator Loss: 2.0371
Epoch 2/2... Discriminator Loss: 0.6490... Generator Loss: 1.5632
Epoch 2/2... Discriminator Loss: 0.7096... Generator Loss: 2.1803
Epoch 2/2... Discriminator Loss: 0.7245... Generator Loss: 1.2953
Epoch 2/2... Discriminator Loss: 0.8499... Generator Loss: 1.1194
Epoch 2/2... Discriminator Loss: 0.6300... Generator Loss: 1.9087
Epoch 2/2... Discriminator Loss: 0.6941... Generator Loss: 2.3455
Epoch 2/2... Discriminator Loss: 0.5445... Generator Loss: 2.4152
Epoch 2/2... Discriminator Loss: 0.7492... Generator Loss: 1.2847
Epoch 2/2... Discriminator Loss: 0.6710... Generator Loss: 1.8995
Epoch 2/2... Discriminator Loss: 0.6627... Generator Loss: 1.5512
Epoch 2/2... Discriminator Loss: 0.5600... Generator Loss: 2.0778
Epoch 2/2... Discriminator Loss: 0.6646... Generator Loss: 1.3999
Epoch 2/2... Discriminator Loss: 0.7980... Generator Loss: 1.2363
Epoch 2/2... Discriminator Loss: 0.5637... Generator Loss: 1.9312
Epoch 2/2... Discriminator Loss: 0.5785... Generator Loss: 1.8833
Epoch 2/2... Discriminator Loss: 0.6922... Generator Loss: 2.6861
Epoch 2/2... Discriminator Loss: 0.7449... Generator Loss: 1.2575
Epoch 2/2... Discriminator Loss: 0.6558... Generator Loss: 1.5479
Epoch 2/2... Discriminator Loss: 0.8532... Generator Loss: 1.0365
Epoch 2/2... Discriminator Loss: 0.6181... Generator Loss: 1.6771
Epoch 2/2... Discriminator Loss: 0.5430... Generator Loss: 2.1412
Epoch 2/2... Discriminator Loss: 0.5850... Generator Loss: 1.8739
Epoch 2/2... Discriminator Loss: 0.5414... Generator Loss: 2.2834
Epoch 2/2... Discriminator Loss: 0.7713... Generator Loss: 1.2121
Epoch 2/2... Discriminator Loss: 0.6974... Generator Loss: 1.3943
Epoch 2/2... Discriminator Loss: 0.5844... Generator Loss: 2.2374
Epoch 2/2... Discriminator Loss: 0.6289... Generator Loss: 1.6136
Epoch 2/2... Discriminator Loss: 0.5344... Generator Loss: 2.0758
Epoch 2/2... Discriminator Loss: 0.5257... Generator Loss: 2.7585
Epoch 2/2... Discriminator Loss: 0.6222... Generator Loss: 1.5931
Epoch 2/2... Discriminator Loss: 0.5052... Generator Loss: 2.8405
Epoch 2/2... Discriminator Loss: 0.5472... Generator Loss: 2.1636
Epoch 2/2... Discriminator Loss: 0.4812... Generator Loss: 2.4328

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [14]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.7292... Generator Loss: 1.4530
Epoch 1/1... Discriminator Loss: 0.6174... Generator Loss: 1.8820
Epoch 1/1... Discriminator Loss: 0.6240... Generator Loss: 1.7932
Epoch 1/1... Discriminator Loss: 0.4474... Generator Loss: 2.6001
Epoch 1/1... Discriminator Loss: 0.6289... Generator Loss: 1.6636
Epoch 1/1... Discriminator Loss: 1.4914... Generator Loss: 0.5041
Epoch 1/1... Discriminator Loss: 0.8063... Generator Loss: 1.5269
Epoch 1/1... Discriminator Loss: 0.8354... Generator Loss: 2.2797
Epoch 1/1... Discriminator Loss: 0.8314... Generator Loss: 1.2629
Epoch 1/1... Discriminator Loss: 0.8197... Generator Loss: 1.7663
Epoch 1/1... Discriminator Loss: 1.1153... Generator Loss: 0.7921
Epoch 1/1... Discriminator Loss: 1.0488... Generator Loss: 0.9712
Epoch 1/1... Discriminator Loss: 0.9065... Generator Loss: 1.0395
Epoch 1/1... Discriminator Loss: 1.0183... Generator Loss: 0.8761
Epoch 1/1... Discriminator Loss: 0.8170... Generator Loss: 1.3942
Epoch 1/1... Discriminator Loss: 1.0282... Generator Loss: 2.6936
Epoch 1/1... Discriminator Loss: 0.9190... Generator Loss: 1.1439
Epoch 1/1... Discriminator Loss: 0.8078... Generator Loss: 1.2411
Epoch 1/1... Discriminator Loss: 1.2437... Generator Loss: 0.6864
Epoch 1/1... Discriminator Loss: 0.8242... Generator Loss: 2.6306
Epoch 1/1... Discriminator Loss: 0.8761... Generator Loss: 1.1490
Epoch 1/1... Discriminator Loss: 1.7126... Generator Loss: 0.3982
Epoch 1/1... Discriminator Loss: 1.1224... Generator Loss: 0.8124
Epoch 1/1... Discriminator Loss: 0.9178... Generator Loss: 1.5591
Epoch 1/1... Discriminator Loss: 1.1263... Generator Loss: 1.1363
Epoch 1/1... Discriminator Loss: 1.6908... Generator Loss: 2.8173
Epoch 1/1... Discriminator Loss: 1.1831... Generator Loss: 0.7667
Epoch 1/1... Discriminator Loss: 0.8109... Generator Loss: 1.3046
Epoch 1/1... Discriminator Loss: 0.9667... Generator Loss: 2.0711
Epoch 1/1... Discriminator Loss: 2.0976... Generator Loss: 0.2214
Epoch 1/1... Discriminator Loss: 1.8529... Generator Loss: 0.3064
Epoch 1/1... Discriminator Loss: 1.0967... Generator Loss: 1.5236
Epoch 1/1... Discriminator Loss: 0.9480... Generator Loss: 1.1857
Epoch 1/1... Discriminator Loss: 1.1684... Generator Loss: 1.7750
Epoch 1/1... Discriminator Loss: 0.7177... Generator Loss: 1.4759
Epoch 1/1... Discriminator Loss: 0.8711... Generator Loss: 1.1299
Epoch 1/1... Discriminator Loss: 1.0593... Generator Loss: 2.2803
Epoch 1/1... Discriminator Loss: 0.7305... Generator Loss: 1.8046
Epoch 1/1... Discriminator Loss: 1.1829... Generator Loss: 0.7944
Epoch 1/1... Discriminator Loss: 0.6144... Generator Loss: 2.0018
Epoch 1/1... Discriminator Loss: 1.7828... Generator Loss: 0.3177
Epoch 1/1... Discriminator Loss: 0.7699... Generator Loss: 1.3489
Epoch 1/1... Discriminator Loss: 1.6441... Generator Loss: 2.2368
Epoch 1/1... Discriminator Loss: 0.9706... Generator Loss: 1.1300
Epoch 1/1... Discriminator Loss: 0.8668... Generator Loss: 1.0648
Epoch 1/1... Discriminator Loss: 0.6968... Generator Loss: 1.4973
Epoch 1/1... Discriminator Loss: 1.1174... Generator Loss: 0.8307
Epoch 1/1... Discriminator Loss: 1.4023... Generator Loss: 0.6124
Epoch 1/1... Discriminator Loss: 0.6887... Generator Loss: 1.6741
Epoch 1/1... Discriminator Loss: 1.1915... Generator Loss: 0.6709
Epoch 1/1... Discriminator Loss: 0.9004... Generator Loss: 1.3560
Epoch 1/1... Discriminator Loss: 1.2859... Generator Loss: 0.5980
Epoch 1/1... Discriminator Loss: 0.9528... Generator Loss: 1.9079
Epoch 1/1... Discriminator Loss: 0.7368... Generator Loss: 1.5423
Epoch 1/1... Discriminator Loss: 1.9924... Generator Loss: 0.2610
Epoch 1/1... Discriminator Loss: 0.9868... Generator Loss: 1.9862
Epoch 1/1... Discriminator Loss: 1.2308... Generator Loss: 1.0163
Epoch 1/1... Discriminator Loss: 0.9057... Generator Loss: 1.5988
Epoch 1/1... Discriminator Loss: 1.5565... Generator Loss: 0.4691
Epoch 1/1... Discriminator Loss: 1.0589... Generator Loss: 0.8305
Epoch 1/1... Discriminator Loss: 1.2348... Generator Loss: 0.7017
Epoch 1/1... Discriminator Loss: 0.9586... Generator Loss: 1.3618
Epoch 1/1... Discriminator Loss: 1.1696... Generator Loss: 0.7204
Epoch 1/1... Discriminator Loss: 1.2437... Generator Loss: 0.8978
Epoch 1/1... Discriminator Loss: 1.1208... Generator Loss: 1.2242
Epoch 1/1... Discriminator Loss: 1.0039... Generator Loss: 0.9111
Epoch 1/1... Discriminator Loss: 1.5345... Generator Loss: 0.6097
Epoch 1/1... Discriminator Loss: 1.2445... Generator Loss: 0.6767
Epoch 1/1... Discriminator Loss: 1.5347... Generator Loss: 0.4468
Epoch 1/1... Discriminator Loss: 1.3620... Generator Loss: 1.5565
Epoch 1/1... Discriminator Loss: 1.1178... Generator Loss: 0.8272
Epoch 1/1... Discriminator Loss: 1.4724... Generator Loss: 1.6452
Epoch 1/1... Discriminator Loss: 1.0223... Generator Loss: 1.1010
Epoch 1/1... Discriminator Loss: 1.5504... Generator Loss: 0.4700
Epoch 1/1... Discriminator Loss: 1.0492... Generator Loss: 1.1954
Epoch 1/1... Discriminator Loss: 0.9703... Generator Loss: 1.2197
Epoch 1/1... Discriminator Loss: 1.1577... Generator Loss: 1.5810
Epoch 1/1... Discriminator Loss: 1.2847... Generator Loss: 0.8838
Epoch 1/1... Discriminator Loss: 1.2297... Generator Loss: 1.3412
Epoch 1/1... Discriminator Loss: 1.0723... Generator Loss: 0.9755
Epoch 1/1... Discriminator Loss: 1.2567... Generator Loss: 0.6644
Epoch 1/1... Discriminator Loss: 1.1752... Generator Loss: 0.6676
Epoch 1/1... Discriminator Loss: 1.1013... Generator Loss: 0.7976
Epoch 1/1... Discriminator Loss: 0.9891... Generator Loss: 1.1527
Epoch 1/1... Discriminator Loss: 0.9432... Generator Loss: 1.3715
Epoch 1/1... Discriminator Loss: 1.2736... Generator Loss: 0.6412
Epoch 1/1... Discriminator Loss: 1.0766... Generator Loss: 0.9643
Epoch 1/1... Discriminator Loss: 1.0030... Generator Loss: 1.2495
Epoch 1/1... Discriminator Loss: 1.2687... Generator Loss: 0.8076
Epoch 1/1... Discriminator Loss: 1.1337... Generator Loss: 1.0314
Epoch 1/1... Discriminator Loss: 1.2360... Generator Loss: 0.6275
Epoch 1/1... Discriminator Loss: 0.9745... Generator Loss: 1.0348
Epoch 1/1... Discriminator Loss: 0.7829... Generator Loss: 1.4978
Epoch 1/1... Discriminator Loss: 1.0793... Generator Loss: 1.0007
Epoch 1/1... Discriminator Loss: 0.9660... Generator Loss: 1.3077
Epoch 1/1... Discriminator Loss: 1.0324... Generator Loss: 0.9540
Epoch 1/1... Discriminator Loss: 1.0697... Generator Loss: 0.7591
Epoch 1/1... Discriminator Loss: 1.1233... Generator Loss: 0.9707
Epoch 1/1... Discriminator Loss: 1.0699... Generator Loss: 0.8502
Epoch 1/1... Discriminator Loss: 0.9470... Generator Loss: 1.0423
Epoch 1/1... Discriminator Loss: 0.9751... Generator Loss: 1.1962
Epoch 1/1... Discriminator Loss: 1.0137... Generator Loss: 0.9317
Epoch 1/1... Discriminator Loss: 0.9013... Generator Loss: 1.1597
Epoch 1/1... Discriminator Loss: 0.7778... Generator Loss: 2.0915
Epoch 1/1... Discriminator Loss: 0.9786... Generator Loss: 1.2019
Epoch 1/1... Discriminator Loss: 1.0081... Generator Loss: 0.9308
Epoch 1/1... Discriminator Loss: 0.8805... Generator Loss: 1.6325
Epoch 1/1... Discriminator Loss: 1.1881... Generator Loss: 0.9319
Epoch 1/1... Discriminator Loss: 0.9839... Generator Loss: 1.0614
Epoch 1/1... Discriminator Loss: 1.1475... Generator Loss: 0.7081
Epoch 1/1... Discriminator Loss: 0.9591... Generator Loss: 1.2611
Epoch 1/1... Discriminator Loss: 1.2078... Generator Loss: 0.6691
Epoch 1/1... Discriminator Loss: 1.3212... Generator Loss: 0.6513
Epoch 1/1... Discriminator Loss: 1.0316... Generator Loss: 0.9278
Epoch 1/1... Discriminator Loss: 0.8693... Generator Loss: 1.4195
Epoch 1/1... Discriminator Loss: 0.9344... Generator Loss: 1.1209
Epoch 1/1... Discriminator Loss: 1.1057... Generator Loss: 0.9315
Epoch 1/1... Discriminator Loss: 1.0487... Generator Loss: 0.8651
Epoch 1/1... Discriminator Loss: 0.7845... Generator Loss: 1.6383
Epoch 1/1... Discriminator Loss: 0.9001... Generator Loss: 1.2994
Epoch 1/1... Discriminator Loss: 1.0913... Generator Loss: 1.3343
Epoch 1/1... Discriminator Loss: 0.8847... Generator Loss: 1.5524
Epoch 1/1... Discriminator Loss: 0.9447... Generator Loss: 1.1925
Epoch 1/1... Discriminator Loss: 1.0575... Generator Loss: 0.9358
Epoch 1/1... Discriminator Loss: 0.9371... Generator Loss: 1.1570
Epoch 1/1... Discriminator Loss: 1.0136... Generator Loss: 1.5942
Epoch 1/1... Discriminator Loss: 1.2347... Generator Loss: 0.6430
Epoch 1/1... Discriminator Loss: 1.2923... Generator Loss: 0.7765
Epoch 1/1... Discriminator Loss: 1.0036... Generator Loss: 0.9655
Epoch 1/1... Discriminator Loss: 1.0019... Generator Loss: 1.0907
Epoch 1/1... Discriminator Loss: 0.7794... Generator Loss: 1.3484
Epoch 1/1... Discriminator Loss: 0.9729... Generator Loss: 1.5391
Epoch 1/1... Discriminator Loss: 1.0015... Generator Loss: 0.8187
Epoch 1/1... Discriminator Loss: 0.8724... Generator Loss: 1.8561
Epoch 1/1... Discriminator Loss: 0.9620... Generator Loss: 0.9261
Epoch 1/1... Discriminator Loss: 1.2060... Generator Loss: 2.6205
Epoch 1/1... Discriminator Loss: 0.9668... Generator Loss: 1.2931
Epoch 1/1... Discriminator Loss: 1.2243... Generator Loss: 0.6415
Epoch 1/1... Discriminator Loss: 1.0110... Generator Loss: 1.1540
Epoch 1/1... Discriminator Loss: 1.1719... Generator Loss: 0.9116
Epoch 1/1... Discriminator Loss: 0.8525... Generator Loss: 1.1964
Epoch 1/1... Discriminator Loss: 1.1117... Generator Loss: 0.8620
Epoch 1/1... Discriminator Loss: 0.9921... Generator Loss: 1.1639
Epoch 1/1... Discriminator Loss: 1.1685... Generator Loss: 0.7604
Epoch 1/1... Discriminator Loss: 1.0175... Generator Loss: 0.8351
Epoch 1/1... Discriminator Loss: 0.9630... Generator Loss: 1.0352
Epoch 1/1... Discriminator Loss: 1.1599... Generator Loss: 0.6734
Epoch 1/1... Discriminator Loss: 1.0327... Generator Loss: 0.9260
Epoch 1/1... Discriminator Loss: 0.9199... Generator Loss: 1.0426
Epoch 1/1... Discriminator Loss: 0.9309... Generator Loss: 1.7299
Epoch 1/1... Discriminator Loss: 0.9410... Generator Loss: 1.3493
Epoch 1/1... Discriminator Loss: 0.9912... Generator Loss: 0.9046
Epoch 1/1... Discriminator Loss: 0.8030... Generator Loss: 1.3790
Epoch 1/1... Discriminator Loss: 0.9140... Generator Loss: 1.1570
Epoch 1/1... Discriminator Loss: 0.9700... Generator Loss: 0.9024
Epoch 1/1... Discriminator Loss: 0.8755... Generator Loss: 1.4363
Epoch 1/1... Discriminator Loss: 1.0609... Generator Loss: 0.8647
Epoch 1/1... Discriminator Loss: 1.1435... Generator Loss: 0.7608
Epoch 1/1... Discriminator Loss: 0.9532... Generator Loss: 1.0416
Epoch 1/1... Discriminator Loss: 0.8857... Generator Loss: 1.7481
Epoch 1/1... Discriminator Loss: 0.9156... Generator Loss: 1.4766
Epoch 1/1... Discriminator Loss: 0.9656... Generator Loss: 1.0030
Epoch 1/1... Discriminator Loss: 0.9735... Generator Loss: 1.0540
Epoch 1/1... Discriminator Loss: 0.8714... Generator Loss: 1.4610
Epoch 1/1... Discriminator Loss: 0.9370... Generator Loss: 1.0395
Epoch 1/1... Discriminator Loss: 1.0308... Generator Loss: 1.0567
Epoch 1/1... Discriminator Loss: 1.1105... Generator Loss: 0.9374
Epoch 1/1... Discriminator Loss: 0.8929... Generator Loss: 0.9766
Epoch 1/1... Discriminator Loss: 0.7486... Generator Loss: 1.4731
Epoch 1/1... Discriminator Loss: 0.8218... Generator Loss: 1.1864
Epoch 1/1... Discriminator Loss: 1.2048... Generator Loss: 0.7027
Epoch 1/1... Discriminator Loss: 1.0137... Generator Loss: 1.1183
Epoch 1/1... Discriminator Loss: 0.9683... Generator Loss: 1.1800
Epoch 1/1... Discriminator Loss: 0.9110... Generator Loss: 1.0172
Epoch 1/1... Discriminator Loss: 0.9019... Generator Loss: 1.2899
Epoch 1/1... Discriminator Loss: 1.3081... Generator Loss: 0.5682
Epoch 1/1... Discriminator Loss: 0.9525... Generator Loss: 1.0175
Epoch 1/1... Discriminator Loss: 1.1539... Generator Loss: 0.7723
Epoch 1/1... Discriminator Loss: 1.3135... Generator Loss: 0.5180
Epoch 1/1... Discriminator Loss: 0.9244... Generator Loss: 1.5807
Epoch 1/1... Discriminator Loss: 1.0413... Generator Loss: 0.9519
Epoch 1/1... Discriminator Loss: 0.8526... Generator Loss: 1.2147
Epoch 1/1... Discriminator Loss: 1.2188... Generator Loss: 1.2818
Epoch 1/1... Discriminator Loss: 0.9252... Generator Loss: 0.9636
Epoch 1/1... Discriminator Loss: 0.8131... Generator Loss: 1.2324
Epoch 1/1... Discriminator Loss: 0.9060... Generator Loss: 1.8314
Epoch 1/1... Discriminator Loss: 0.9774... Generator Loss: 1.0243
Epoch 1/1... Discriminator Loss: 1.2111... Generator Loss: 0.6487
Epoch 1/1... Discriminator Loss: 0.9343... Generator Loss: 1.1162
Epoch 1/1... Discriminator Loss: 0.8526... Generator Loss: 1.0503
Epoch 1/1... Discriminator Loss: 1.1680... Generator Loss: 1.8182
Epoch 1/1... Discriminator Loss: 0.8664... Generator Loss: 1.0407
Epoch 1/1... Discriminator Loss: 0.8185... Generator Loss: 1.4873
Epoch 1/1... Discriminator Loss: 0.8012... Generator Loss: 1.4727
Epoch 1/1... Discriminator Loss: 1.0748... Generator Loss: 1.0511
Epoch 1/1... Discriminator Loss: 0.7414... Generator Loss: 1.3869
Epoch 1/1... Discriminator Loss: 1.1141... Generator Loss: 1.0229
Epoch 1/1... Discriminator Loss: 0.9429... Generator Loss: 0.9278
Epoch 1/1... Discriminator Loss: 0.9340... Generator Loss: 1.3258
Epoch 1/1... Discriminator Loss: 0.8789... Generator Loss: 1.8541
Epoch 1/1... Discriminator Loss: 0.8556... Generator Loss: 1.1643
Epoch 1/1... Discriminator Loss: 0.9373... Generator Loss: 1.0572
Epoch 1/1... Discriminator Loss: 1.0269... Generator Loss: 0.9739
Epoch 1/1... Discriminator Loss: 0.9097... Generator Loss: 1.0988
Epoch 1/1... Discriminator Loss: 1.1702... Generator Loss: 2.0005
Epoch 1/1... Discriminator Loss: 0.8358... Generator Loss: 1.1458
Epoch 1/1... Discriminator Loss: 1.5362... Generator Loss: 0.4120
Epoch 1/1... Discriminator Loss: 1.3836... Generator Loss: 0.5205
Epoch 1/1... Discriminator Loss: 0.8908... Generator Loss: 1.3724
Epoch 1/1... Discriminator Loss: 0.9691... Generator Loss: 0.8982
Epoch 1/1... Discriminator Loss: 1.0186... Generator Loss: 1.0512
Epoch 1/1... Discriminator Loss: 0.8486... Generator Loss: 1.2877
Epoch 1/1... Discriminator Loss: 0.8112... Generator Loss: 1.4631
Epoch 1/1... Discriminator Loss: 1.1302... Generator Loss: 0.7024
Epoch 1/1... Discriminator Loss: 0.9408... Generator Loss: 1.1175
Epoch 1/1... Discriminator Loss: 1.5120... Generator Loss: 0.4068
Epoch 1/1... Discriminator Loss: 0.9860... Generator Loss: 0.9949
Epoch 1/1... Discriminator Loss: 1.1299... Generator Loss: 0.7372
Epoch 1/1... Discriminator Loss: 0.9996... Generator Loss: 1.1070
Epoch 1/1... Discriminator Loss: 0.9427... Generator Loss: 1.1066
Epoch 1/1... Discriminator Loss: 1.5179... Generator Loss: 0.4915
Epoch 1/1... Discriminator Loss: 1.0864... Generator Loss: 0.8906
Epoch 1/1... Discriminator Loss: 1.0459... Generator Loss: 0.9142
Epoch 1/1... Discriminator Loss: 1.1251... Generator Loss: 0.7225
Epoch 1/1... Discriminator Loss: 1.0384... Generator Loss: 0.8828
Epoch 1/1... Discriminator Loss: 0.8298... Generator Loss: 1.3879
Epoch 1/1... Discriminator Loss: 0.9004... Generator Loss: 1.0791
Epoch 1/1... Discriminator Loss: 1.5074... Generator Loss: 0.4441
Epoch 1/1... Discriminator Loss: 1.1315... Generator Loss: 0.8707
Epoch 1/1... Discriminator Loss: 0.9054... Generator Loss: 1.6486
Epoch 1/1... Discriminator Loss: 0.8426... Generator Loss: 1.1966
Epoch 1/1... Discriminator Loss: 1.4709... Generator Loss: 0.5079
Epoch 1/1... Discriminator Loss: 0.9237... Generator Loss: 1.0642
Epoch 1/1... Discriminator Loss: 1.0980... Generator Loss: 0.7397
Epoch 1/1... Discriminator Loss: 1.3196... Generator Loss: 0.5467
Epoch 1/1... Discriminator Loss: 0.7506... Generator Loss: 1.3059
Epoch 1/1... Discriminator Loss: 1.2129... Generator Loss: 0.6684
Epoch 1/1... Discriminator Loss: 0.9010... Generator Loss: 1.8471
Epoch 1/1... Discriminator Loss: 0.9623... Generator Loss: 1.4423
Epoch 1/1... Discriminator Loss: 1.0216... Generator Loss: 0.9375
Epoch 1/1... Discriminator Loss: 1.0124... Generator Loss: 0.8815
Epoch 1/1... Discriminator Loss: 1.1238... Generator Loss: 1.1179
Epoch 1/1... Discriminator Loss: 0.8980... Generator Loss: 1.4078
Epoch 1/1... Discriminator Loss: 1.4856... Generator Loss: 0.4732
Epoch 1/1... Discriminator Loss: 0.9955... Generator Loss: 1.3278
Epoch 1/1... Discriminator Loss: 0.9283... Generator Loss: 1.1748
Epoch 1/1... Discriminator Loss: 0.8538... Generator Loss: 1.0151
Epoch 1/1... Discriminator Loss: 0.9769... Generator Loss: 0.8248
Epoch 1/1... Discriminator Loss: 0.8426... Generator Loss: 1.5263
Epoch 1/1... Discriminator Loss: 0.9023... Generator Loss: 1.4067
Epoch 1/1... Discriminator Loss: 0.9466... Generator Loss: 1.1796
Epoch 1/1... Discriminator Loss: 1.0712... Generator Loss: 0.8753
Epoch 1/1... Discriminator Loss: 0.8719... Generator Loss: 1.0816
Epoch 1/1... Discriminator Loss: 0.9608... Generator Loss: 0.8771
Epoch 1/1... Discriminator Loss: 1.0716... Generator Loss: 0.8450
Epoch 1/1... Discriminator Loss: 1.3316... Generator Loss: 0.5105
Epoch 1/1... Discriminator Loss: 1.3929... Generator Loss: 0.4829
Epoch 1/1... Discriminator Loss: 0.9213... Generator Loss: 1.2244
Epoch 1/1... Discriminator Loss: 1.4038... Generator Loss: 0.4758
Epoch 1/1... Discriminator Loss: 1.2480... Generator Loss: 0.6248
Epoch 1/1... Discriminator Loss: 1.0525... Generator Loss: 0.8704
Epoch 1/1... Discriminator Loss: 1.1056... Generator Loss: 0.7518
Epoch 1/1... Discriminator Loss: 0.8831... Generator Loss: 1.3419
Epoch 1/1... Discriminator Loss: 0.8102... Generator Loss: 1.4843
Epoch 1/1... Discriminator Loss: 1.0048... Generator Loss: 0.8591
Epoch 1/1... Discriminator Loss: 0.9287... Generator Loss: 1.8043
Epoch 1/1... Discriminator Loss: 0.9728... Generator Loss: 0.9012
Epoch 1/1... Discriminator Loss: 0.9874... Generator Loss: 0.8481
Epoch 1/1... Discriminator Loss: 0.9869... Generator Loss: 1.1571
Epoch 1/1... Discriminator Loss: 1.0356... Generator Loss: 0.8138
Epoch 1/1... Discriminator Loss: 1.3034... Generator Loss: 0.6118
Epoch 1/1... Discriminator Loss: 0.9591... Generator Loss: 0.9936
Epoch 1/1... Discriminator Loss: 0.7071... Generator Loss: 1.4822
Epoch 1/1... Discriminator Loss: 1.1393... Generator Loss: 1.5220
Epoch 1/1... Discriminator Loss: 0.9436... Generator Loss: 0.9299
Epoch 1/1... Discriminator Loss: 0.7462... Generator Loss: 1.2451
Epoch 1/1... Discriminator Loss: 0.9546... Generator Loss: 1.5921
Epoch 1/1... Discriminator Loss: 1.5885... Generator Loss: 0.4008
Epoch 1/1... Discriminator Loss: 0.9382... Generator Loss: 1.4216
Epoch 1/1... Discriminator Loss: 1.0112... Generator Loss: 0.9037
Epoch 1/1... Discriminator Loss: 0.9414... Generator Loss: 1.2654
Epoch 1/1... Discriminator Loss: 0.9816... Generator Loss: 0.9589
Epoch 1/1... Discriminator Loss: 0.7324... Generator Loss: 1.3369
Epoch 1/1... Discriminator Loss: 1.0804... Generator Loss: 0.7569
Epoch 1/1... Discriminator Loss: 0.9568... Generator Loss: 0.8738
Epoch 1/1... Discriminator Loss: 1.1798... Generator Loss: 0.7413
Epoch 1/1... Discriminator Loss: 1.2573... Generator Loss: 0.5855
Epoch 1/1... Discriminator Loss: 1.1281... Generator Loss: 0.6578
Epoch 1/1... Discriminator Loss: 1.0138... Generator Loss: 0.8895
Epoch 1/1... Discriminator Loss: 0.8447... Generator Loss: 1.3458
Epoch 1/1... Discriminator Loss: 0.8217... Generator Loss: 1.1894
Epoch 1/1... Discriminator Loss: 0.8364... Generator Loss: 1.1276
Epoch 1/1... Discriminator Loss: 1.6059... Generator Loss: 0.3948
Epoch 1/1... Discriminator Loss: 1.4110... Generator Loss: 0.4899
Epoch 1/1... Discriminator Loss: 0.7283... Generator Loss: 1.3198
Epoch 1/1... Discriminator Loss: 0.7925... Generator Loss: 1.2492
Epoch 1/1... Discriminator Loss: 0.8437... Generator Loss: 1.3485
Epoch 1/1... Discriminator Loss: 1.4939... Generator Loss: 0.4378
Epoch 1/1... Discriminator Loss: 0.8205... Generator Loss: 1.5488
Epoch 1/1... Discriminator Loss: 0.8198... Generator Loss: 1.1783
Epoch 1/1... Discriminator Loss: 0.7982... Generator Loss: 1.3767
Epoch 1/1... Discriminator Loss: 0.9316... Generator Loss: 1.0682
Epoch 1/1... Discriminator Loss: 0.6652... Generator Loss: 1.5461
Epoch 1/1... Discriminator Loss: 0.9806... Generator Loss: 1.3040
Epoch 1/1... Discriminator Loss: 0.8460... Generator Loss: 1.2588
Epoch 1/1... Discriminator Loss: 0.8321... Generator Loss: 1.3262
Epoch 1/1... Discriminator Loss: 1.1397... Generator Loss: 0.7024
Epoch 1/1... Discriminator Loss: 1.4106... Generator Loss: 0.4687
Epoch 1/1... Discriminator Loss: 0.7672... Generator Loss: 1.4469
Epoch 1/1... Discriminator Loss: 1.3029... Generator Loss: 0.5801
Epoch 1/1... Discriminator Loss: 1.0851... Generator Loss: 0.8579
Epoch 1/1... Discriminator Loss: 1.0999... Generator Loss: 0.7421
Epoch 1/1... Discriminator Loss: 0.8184... Generator Loss: 1.5752
Epoch 1/1... Discriminator Loss: 0.9597... Generator Loss: 1.1845
Epoch 1/1... Discriminator Loss: 0.9846... Generator Loss: 1.0891
Epoch 1/1... Discriminator Loss: 0.8965... Generator Loss: 1.0427
Epoch 1/1... Discriminator Loss: 1.1291... Generator Loss: 0.6736
Epoch 1/1... Discriminator Loss: 0.8662... Generator Loss: 1.4814
Epoch 1/1... Discriminator Loss: 1.0161... Generator Loss: 1.0143
Epoch 1/1... Discriminator Loss: 0.7115... Generator Loss: 1.4933
Epoch 1/1... Discriminator Loss: 1.3130... Generator Loss: 0.5376
Epoch 1/1... Discriminator Loss: 0.8632... Generator Loss: 1.2867
Epoch 1/1... Discriminator Loss: 0.8106... Generator Loss: 1.1310
Epoch 1/1... Discriminator Loss: 0.8296... Generator Loss: 1.2185
Epoch 1/1... Discriminator Loss: 1.0268... Generator Loss: 1.1045
Epoch 1/1... Discriminator Loss: 0.8579... Generator Loss: 1.9729
Epoch 1/1... Discriminator Loss: 0.6566... Generator Loss: 2.3132
Epoch 1/1... Discriminator Loss: 1.0867... Generator Loss: 0.9418
Epoch 1/1... Discriminator Loss: 0.8846... Generator Loss: 0.9774
Epoch 1/1... Discriminator Loss: 0.8537... Generator Loss: 1.1202
Epoch 1/1... Discriminator Loss: 0.7941... Generator Loss: 1.1754
Epoch 1/1... Discriminator Loss: 0.9622... Generator Loss: 1.0416
Epoch 1/1... Discriminator Loss: 1.1884... Generator Loss: 1.1288
Epoch 1/1... Discriminator Loss: 1.1034... Generator Loss: 0.7765
Epoch 1/1... Discriminator Loss: 0.7224... Generator Loss: 1.3983
Epoch 1/1... Discriminator Loss: 1.3615... Generator Loss: 0.4942
Epoch 1/1... Discriminator Loss: 0.8698... Generator Loss: 1.2877
Epoch 1/1... Discriminator Loss: 0.6944... Generator Loss: 1.9596
Epoch 1/1... Discriminator Loss: 1.2333... Generator Loss: 1.4114
Epoch 1/1... Discriminator Loss: 0.7488... Generator Loss: 1.3239
Epoch 1/1... Discriminator Loss: 1.2195... Generator Loss: 0.6550
Epoch 1/1... Discriminator Loss: 0.8418... Generator Loss: 1.2813
Epoch 1/1... Discriminator Loss: 0.5779... Generator Loss: 2.2434
Epoch 1/1... Discriminator Loss: 0.9517... Generator Loss: 1.2535
Epoch 1/1... Discriminator Loss: 0.7481... Generator Loss: 1.2762
Epoch 1/1... Discriminator Loss: 1.0215... Generator Loss: 0.8818
Epoch 1/1... Discriminator Loss: 0.8982... Generator Loss: 1.1107
Epoch 1/1... Discriminator Loss: 1.0234... Generator Loss: 0.8042
Epoch 1/1... Discriminator Loss: 0.8873... Generator Loss: 1.1601
Epoch 1/1... Discriminator Loss: 0.7395... Generator Loss: 1.4174
Epoch 1/1... Discriminator Loss: 1.2794... Generator Loss: 0.5686
Epoch 1/1... Discriminator Loss: 0.7730... Generator Loss: 1.5089
Epoch 1/1... Discriminator Loss: 0.9385... Generator Loss: 0.9249
Epoch 1/1... Discriminator Loss: 0.6950... Generator Loss: 2.0826
Epoch 1/1... Discriminator Loss: 0.7075... Generator Loss: 1.3735
Epoch 1/1... Discriminator Loss: 0.9468... Generator Loss: 1.4051
Epoch 1/1... Discriminator Loss: 1.0454... Generator Loss: 0.8197
Epoch 1/1... Discriminator Loss: 0.9008... Generator Loss: 1.3496
Epoch 1/1... Discriminator Loss: 0.9709... Generator Loss: 0.9019
Epoch 1/1... Discriminator Loss: 0.9097... Generator Loss: 1.0666
Epoch 1/1... Discriminator Loss: 0.8528... Generator Loss: 1.5460
Epoch 1/1... Discriminator Loss: 0.6982... Generator Loss: 1.3952
Epoch 1/1... Discriminator Loss: 0.7298... Generator Loss: 1.4819
Epoch 1/1... Discriminator Loss: 1.2262... Generator Loss: 0.6543
Epoch 1/1... Discriminator Loss: 0.8580... Generator Loss: 1.1877
Epoch 1/1... Discriminator Loss: 1.0447... Generator Loss: 0.9948
Epoch 1/1... Discriminator Loss: 0.7839... Generator Loss: 1.2906
Epoch 1/1... Discriminator Loss: 1.1247... Generator Loss: 0.6898
Epoch 1/1... Discriminator Loss: 0.8660... Generator Loss: 1.1028
Epoch 1/1... Discriminator Loss: 1.0200... Generator Loss: 1.1621
Epoch 1/1... Discriminator Loss: 1.0098... Generator Loss: 0.8143
Epoch 1/1... Discriminator Loss: 0.9740... Generator Loss: 0.8769
Epoch 1/1... Discriminator Loss: 1.4060... Generator Loss: 0.5641
Epoch 1/1... Discriminator Loss: 1.1085... Generator Loss: 0.8056
Epoch 1/1... Discriminator Loss: 0.9746... Generator Loss: 1.2709
Epoch 1/1... Discriminator Loss: 0.7935... Generator Loss: 1.4746
Epoch 1/1... Discriminator Loss: 1.0378... Generator Loss: 0.8662
Epoch 1/1... Discriminator Loss: 1.0330... Generator Loss: 0.8339
Epoch 1/1... Discriminator Loss: 1.1494... Generator Loss: 0.7296
Epoch 1/1... Discriminator Loss: 1.2212... Generator Loss: 0.6373
Epoch 1/1... Discriminator Loss: 1.0228... Generator Loss: 1.3237
Epoch 1/1... Discriminator Loss: 1.0096... Generator Loss: 0.8580
Epoch 1/1... Discriminator Loss: 0.7598... Generator Loss: 1.2926
Epoch 1/1... Discriminator Loss: 1.3078... Generator Loss: 0.6451
Epoch 1/1... Discriminator Loss: 1.0280... Generator Loss: 0.9283
Epoch 1/1... Discriminator Loss: 0.9622... Generator Loss: 0.9382
Epoch 1/1... Discriminator Loss: 1.3858... Generator Loss: 0.5073
Epoch 1/1... Discriminator Loss: 0.9400... Generator Loss: 1.0186
Epoch 1/1... Discriminator Loss: 1.0449... Generator Loss: 0.8762
Epoch 1/1... Discriminator Loss: 0.7933... Generator Loss: 1.1973
Epoch 1/1... Discriminator Loss: 0.9686... Generator Loss: 1.0869
Epoch 1/1... Discriminator Loss: 0.7582... Generator Loss: 1.4748
Epoch 1/1... Discriminator Loss: 0.8664... Generator Loss: 1.1581
Epoch 1/1... Discriminator Loss: 0.9488... Generator Loss: 1.1914
Epoch 1/1... Discriminator Loss: 0.6987... Generator Loss: 1.5066
Epoch 1/1... Discriminator Loss: 0.8296... Generator Loss: 1.1027
Epoch 1/1... Discriminator Loss: 0.9068... Generator Loss: 1.0456
Epoch 1/1... Discriminator Loss: 0.9886... Generator Loss: 1.0867
Epoch 1/1... Discriminator Loss: 0.7452... Generator Loss: 1.4444
Epoch 1/1... Discriminator Loss: 0.9283... Generator Loss: 1.2933
Epoch 1/1... Discriminator Loss: 1.0821... Generator Loss: 2.1050
Epoch 1/1... Discriminator Loss: 1.0416... Generator Loss: 0.9495
Epoch 1/1... Discriminator Loss: 0.8218... Generator Loss: 1.1275
Epoch 1/1... Discriminator Loss: 0.8437... Generator Loss: 1.2470
Epoch 1/1... Discriminator Loss: 0.7006... Generator Loss: 1.5874
Epoch 1/1... Discriminator Loss: 1.0957... Generator Loss: 2.8378
Epoch 1/1... Discriminator Loss: 0.9538... Generator Loss: 0.9781
Epoch 1/1... Discriminator Loss: 1.0601... Generator Loss: 0.8556
Epoch 1/1... Discriminator Loss: 0.8062... Generator Loss: 1.3326
Epoch 1/1... Discriminator Loss: 0.9109... Generator Loss: 1.0227
Epoch 1/1... Discriminator Loss: 0.9345... Generator Loss: 0.9626
Epoch 1/1... Discriminator Loss: 0.8045... Generator Loss: 1.3991
Epoch 1/1... Discriminator Loss: 0.8423... Generator Loss: 1.2072
Epoch 1/1... Discriminator Loss: 0.7350... Generator Loss: 1.6578
Epoch 1/1... Discriminator Loss: 0.8482... Generator Loss: 1.4098
Epoch 1/1... Discriminator Loss: 0.6457... Generator Loss: 1.6447
Epoch 1/1... Discriminator Loss: 0.6446... Generator Loss: 1.6328
Epoch 1/1... Discriminator Loss: 0.7480... Generator Loss: 1.2888
Epoch 1/1... Discriminator Loss: 1.4201... Generator Loss: 0.4870
Epoch 1/1... Discriminator Loss: 0.6738... Generator Loss: 1.6434
Epoch 1/1... Discriminator Loss: 1.0818... Generator Loss: 0.7923
Epoch 1/1... Discriminator Loss: 0.8986... Generator Loss: 1.3908
Epoch 1/1... Discriminator Loss: 1.0473... Generator Loss: 0.8769
Epoch 1/1... Discriminator Loss: 1.1414... Generator Loss: 1.0648
Epoch 1/1... Discriminator Loss: 1.2965... Generator Loss: 0.5612
Epoch 1/1... Discriminator Loss: 0.8245... Generator Loss: 1.2499
Epoch 1/1... Discriminator Loss: 1.0561... Generator Loss: 0.7688
Epoch 1/1... Discriminator Loss: 0.8484... Generator Loss: 1.2242
Epoch 1/1... Discriminator Loss: 0.9429... Generator Loss: 1.1118
Epoch 1/1... Discriminator Loss: 0.8751... Generator Loss: 1.7547
Epoch 1/1... Discriminator Loss: 1.0769... Generator Loss: 1.8225
Epoch 1/1... Discriminator Loss: 0.9208... Generator Loss: 2.0271
Epoch 1/1... Discriminator Loss: 0.8529... Generator Loss: 1.2772
Epoch 1/1... Discriminator Loss: 0.7510... Generator Loss: 1.5577
Epoch 1/1... Discriminator Loss: 1.0058... Generator Loss: 0.9602
Epoch 1/1... Discriminator Loss: 0.9553... Generator Loss: 1.0610
Epoch 1/1... Discriminator Loss: 1.1873... Generator Loss: 1.9443
Epoch 1/1... Discriminator Loss: 1.1774... Generator Loss: 0.7351
Epoch 1/1... Discriminator Loss: 0.8638... Generator Loss: 1.1957
Epoch 1/1... Discriminator Loss: 1.4676... Generator Loss: 0.4421
Epoch 1/1... Discriminator Loss: 1.0502... Generator Loss: 0.7880
Epoch 1/1... Discriminator Loss: 1.2027... Generator Loss: 0.6628
Epoch 1/1... Discriminator Loss: 1.3266... Generator Loss: 0.5480
Epoch 1/1... Discriminator Loss: 1.0130... Generator Loss: 0.9578
Epoch 1/1... Discriminator Loss: 1.0975... Generator Loss: 0.6976
Epoch 1/1... Discriminator Loss: 0.7763... Generator Loss: 1.2777
Epoch 1/1... Discriminator Loss: 1.0781... Generator Loss: 0.8286
Epoch 1/1... Discriminator Loss: 0.9863... Generator Loss: 0.9374
Epoch 1/1... Discriminator Loss: 0.9412... Generator Loss: 1.4765
Epoch 1/1... Discriminator Loss: 0.8900... Generator Loss: 1.0943
Epoch 1/1... Discriminator Loss: 1.0267... Generator Loss: 0.8335
Epoch 1/1... Discriminator Loss: 1.1599... Generator Loss: 0.7048
Epoch 1/1... Discriminator Loss: 0.9478... Generator Loss: 0.9967
Epoch 1/1... Discriminator Loss: 1.0773... Generator Loss: 1.0960
Epoch 1/1... Discriminator Loss: 0.7730... Generator Loss: 1.7224
Epoch 1/1... Discriminator Loss: 0.7211... Generator Loss: 1.4675
Epoch 1/1... Discriminator Loss: 0.9536... Generator Loss: 0.9046
Epoch 1/1... Discriminator Loss: 0.9491... Generator Loss: 0.8818
Epoch 1/1... Discriminator Loss: 0.9068... Generator Loss: 1.2756
Epoch 1/1... Discriminator Loss: 0.7689... Generator Loss: 1.6505
Epoch 1/1... Discriminator Loss: 0.8761... Generator Loss: 1.0747
Epoch 1/1... Discriminator Loss: 0.9939... Generator Loss: 2.0718
Epoch 1/1... Discriminator Loss: 2.1693... Generator Loss: 0.2307
Epoch 1/1... Discriminator Loss: 0.8856... Generator Loss: 1.3261
Epoch 1/1... Discriminator Loss: 1.3555... Generator Loss: 0.5134
Epoch 1/1... Discriminator Loss: 1.1680... Generator Loss: 0.6883
Epoch 1/1... Discriminator Loss: 1.1928... Generator Loss: 1.5144
Epoch 1/1... Discriminator Loss: 1.1868... Generator Loss: 0.6776
Epoch 1/1... Discriminator Loss: 0.9319... Generator Loss: 1.5445
Epoch 1/1... Discriminator Loss: 1.0124... Generator Loss: 0.8784
Epoch 1/1... Discriminator Loss: 1.0010... Generator Loss: 1.6997
Epoch 1/1... Discriminator Loss: 0.9856... Generator Loss: 0.8568
Epoch 1/1... Discriminator Loss: 1.3307... Generator Loss: 2.1578
Epoch 1/1... Discriminator Loss: 0.6859... Generator Loss: 1.7202
Epoch 1/1... Discriminator Loss: 1.8843... Generator Loss: 0.2922
Epoch 1/1... Discriminator Loss: 0.9428... Generator Loss: 1.3121
Epoch 1/1... Discriminator Loss: 0.8776... Generator Loss: 1.0973
Epoch 1/1... Discriminator Loss: 1.2534... Generator Loss: 0.8578
Epoch 1/1... Discriminator Loss: 0.6819... Generator Loss: 1.7921
Epoch 1/1... Discriminator Loss: 0.6882... Generator Loss: 1.6753
Epoch 1/1... Discriminator Loss: 1.4723... Generator Loss: 0.4972
Epoch 1/1... Discriminator Loss: 1.0759... Generator Loss: 1.2267
Epoch 1/1... Discriminator Loss: 0.8096... Generator Loss: 1.4452
Epoch 1/1... Discriminator Loss: 0.9661... Generator Loss: 1.0526
Epoch 1/1... Discriminator Loss: 0.9732... Generator Loss: 0.9359
Epoch 1/1... Discriminator Loss: 1.2044... Generator Loss: 0.6224
Epoch 1/1... Discriminator Loss: 1.0619... Generator Loss: 1.1686
Epoch 1/1... Discriminator Loss: 0.9659... Generator Loss: 1.1930
Epoch 1/1... Discriminator Loss: 1.0078... Generator Loss: 1.4651
Epoch 1/1... Discriminator Loss: 1.1979... Generator Loss: 0.6320
Epoch 1/1... Discriminator Loss: 1.1934... Generator Loss: 0.7022
Epoch 1/1... Discriminator Loss: 1.0238... Generator Loss: 0.7817
Epoch 1/1... Discriminator Loss: 1.1045... Generator Loss: 0.7112
Epoch 1/1... Discriminator Loss: 0.9487... Generator Loss: 0.8904
Epoch 1/1... Discriminator Loss: 0.8455... Generator Loss: 1.6317
Epoch 1/1... Discriminator Loss: 0.7144... Generator Loss: 1.4027
Epoch 1/1... Discriminator Loss: 0.8023... Generator Loss: 1.2847
Epoch 1/1... Discriminator Loss: 0.8243... Generator Loss: 1.4855
Epoch 1/1... Discriminator Loss: 0.7954... Generator Loss: 1.4730
Epoch 1/1... Discriminator Loss: 1.1996... Generator Loss: 0.6525
Epoch 1/1... Discriminator Loss: 0.8426... Generator Loss: 1.1065
Epoch 1/1... Discriminator Loss: 0.9998... Generator Loss: 0.9946
Epoch 1/1... Discriminator Loss: 1.0399... Generator Loss: 0.9668
Epoch 1/1... Discriminator Loss: 0.8044... Generator Loss: 1.2345
Epoch 1/1... Discriminator Loss: 1.1705... Generator Loss: 0.7102
Epoch 1/1... Discriminator Loss: 1.4929... Generator Loss: 0.4835
Epoch 1/1... Discriminator Loss: 1.1468... Generator Loss: 0.6641
Epoch 1/1... Discriminator Loss: 1.0820... Generator Loss: 0.7075
Epoch 1/1... Discriminator Loss: 0.8128... Generator Loss: 1.4812
Epoch 1/1... Discriminator Loss: 0.9943... Generator Loss: 1.0887
Epoch 1/1... Discriminator Loss: 0.7390... Generator Loss: 1.4270
Epoch 1/1... Discriminator Loss: 1.4835... Generator Loss: 0.4404
Epoch 1/1... Discriminator Loss: 0.7517... Generator Loss: 1.4557
Epoch 1/1... Discriminator Loss: 1.5313... Generator Loss: 0.4166
Epoch 1/1... Discriminator Loss: 0.6903... Generator Loss: 1.5674
Epoch 1/1... Discriminator Loss: 0.7912... Generator Loss: 1.3821
Epoch 1/1... Discriminator Loss: 1.2257... Generator Loss: 0.6190
Epoch 1/1... Discriminator Loss: 0.9969... Generator Loss: 0.8310
Epoch 1/1... Discriminator Loss: 0.9505... Generator Loss: 0.9569
Epoch 1/1... Discriminator Loss: 1.1989... Generator Loss: 0.6291
Epoch 1/1... Discriminator Loss: 1.1687... Generator Loss: 0.6467
Epoch 1/1... Discriminator Loss: 0.7191... Generator Loss: 1.7240
Epoch 1/1... Discriminator Loss: 0.7666... Generator Loss: 1.4732
Epoch 1/1... Discriminator Loss: 0.9010... Generator Loss: 0.9780
Epoch 1/1... Discriminator Loss: 0.8138... Generator Loss: 1.3896
Epoch 1/1... Discriminator Loss: 0.8340... Generator Loss: 1.3751
Epoch 1/1... Discriminator Loss: 0.8811... Generator Loss: 1.0874
Epoch 1/1... Discriminator Loss: 1.1131... Generator Loss: 0.7354
Epoch 1/1... Discriminator Loss: 1.0933... Generator Loss: 0.8953
Epoch 1/1... Discriminator Loss: 0.9364... Generator Loss: 0.8918
Epoch 1/1... Discriminator Loss: 0.9443... Generator Loss: 1.4260
Epoch 1/1... Discriminator Loss: 0.9744... Generator Loss: 0.9415
Epoch 1/1... Discriminator Loss: 1.0642... Generator Loss: 0.7910
Epoch 1/1... Discriminator Loss: 0.9522... Generator Loss: 1.2900
Epoch 1/1... Discriminator Loss: 1.2680... Generator Loss: 0.5622
Epoch 1/1... Discriminator Loss: 0.9733... Generator Loss: 0.8835
Epoch 1/1... Discriminator Loss: 0.7642... Generator Loss: 1.6035
Epoch 1/1... Discriminator Loss: 0.8593... Generator Loss: 1.4891
Epoch 1/1... Discriminator Loss: 0.9803... Generator Loss: 1.1023
Epoch 1/1... Discriminator Loss: 0.8671... Generator Loss: 1.2933
Epoch 1/1... Discriminator Loss: 1.2583... Generator Loss: 0.5686
Epoch 1/1... Discriminator Loss: 0.7643... Generator Loss: 1.2676
Epoch 1/1... Discriminator Loss: 0.7768... Generator Loss: 1.3049
Epoch 1/1... Discriminator Loss: 1.1592... Generator Loss: 1.3314
Epoch 1/1... Discriminator Loss: 1.0205... Generator Loss: 0.8414
Epoch 1/1... Discriminator Loss: 0.5114... Generator Loss: 2.1474
Epoch 1/1... Discriminator Loss: 1.0412... Generator Loss: 0.9869
Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 0.5326
Epoch 1/1... Discriminator Loss: 1.1792... Generator Loss: 0.7167
Epoch 1/1... Discriminator Loss: 0.9201... Generator Loss: 0.9646
Epoch 1/1... Discriminator Loss: 0.7864... Generator Loss: 2.3416
Epoch 1/1... Discriminator Loss: 0.7740... Generator Loss: 1.3307
Epoch 1/1... Discriminator Loss: 0.6242... Generator Loss: 1.5479
Epoch 1/1... Discriminator Loss: 0.8945... Generator Loss: 1.5033
Epoch 1/1... Discriminator Loss: 1.0725... Generator Loss: 0.7969
Epoch 1/1... Discriminator Loss: 1.2884... Generator Loss: 0.5589
Epoch 1/1... Discriminator Loss: 1.0823... Generator Loss: 0.7615
Epoch 1/1... Discriminator Loss: 0.7622... Generator Loss: 1.2407
Epoch 1/1... Discriminator Loss: 0.5766... Generator Loss: 1.9348
Epoch 1/1... Discriminator Loss: 0.9038... Generator Loss: 1.0536
Epoch 1/1... Discriminator Loss: 0.8494... Generator Loss: 2.0204
Epoch 1/1... Discriminator Loss: 0.8713... Generator Loss: 1.0637
Epoch 1/1... Discriminator Loss: 1.3438... Generator Loss: 0.5466
Epoch 1/1... Discriminator Loss: 0.8648... Generator Loss: 1.0633
Epoch 1/1... Discriminator Loss: 0.8960... Generator Loss: 1.0731
Epoch 1/1... Discriminator Loss: 1.2399... Generator Loss: 0.6204
Epoch 1/1... Discriminator Loss: 0.9030... Generator Loss: 1.4169
Epoch 1/1... Discriminator Loss: 0.8803... Generator Loss: 1.0863
Epoch 1/1... Discriminator Loss: 0.8038... Generator Loss: 1.1699
Epoch 1/1... Discriminator Loss: 0.6937... Generator Loss: 1.6379
Epoch 1/1... Discriminator Loss: 0.8143... Generator Loss: 1.2206
Epoch 1/1... Discriminator Loss: 1.1759... Generator Loss: 0.6528
Epoch 1/1... Discriminator Loss: 0.8918... Generator Loss: 1.1417
Epoch 1/1... Discriminator Loss: 0.7641... Generator Loss: 1.7173
Epoch 1/1... Discriminator Loss: 1.1776... Generator Loss: 0.7647
Epoch 1/1... Discriminator Loss: 0.7913... Generator Loss: 1.7529
Epoch 1/1... Discriminator Loss: 0.9564... Generator Loss: 0.9169
Epoch 1/1... Discriminator Loss: 1.0918... Generator Loss: 0.8831
Epoch 1/1... Discriminator Loss: 0.8208... Generator Loss: 1.3453
Epoch 1/1... Discriminator Loss: 1.4724... Generator Loss: 0.6491
Epoch 1/1... Discriminator Loss: 1.1580... Generator Loss: 0.7114
Epoch 1/1... Discriminator Loss: 1.1507... Generator Loss: 0.6970
Epoch 1/1... Discriminator Loss: 0.7255... Generator Loss: 1.2394
Epoch 1/1... Discriminator Loss: 1.4426... Generator Loss: 0.4736
Epoch 1/1... Discriminator Loss: 1.3282... Generator Loss: 0.5411
Epoch 1/1... Discriminator Loss: 1.0616... Generator Loss: 0.8070
Epoch 1/1... Discriminator Loss: 1.0508... Generator Loss: 0.8397
Epoch 1/1... Discriminator Loss: 0.9837... Generator Loss: 0.8616
Epoch 1/1... Discriminator Loss: 1.2094... Generator Loss: 0.6894
Epoch 1/1... Discriminator Loss: 0.8397... Generator Loss: 1.1614
Epoch 1/1... Discriminator Loss: 0.7841... Generator Loss: 1.1690
Epoch 1/1... Discriminator Loss: 1.4534... Generator Loss: 0.4563
Epoch 1/1... Discriminator Loss: 0.7362... Generator Loss: 1.4382
Epoch 1/1... Discriminator Loss: 1.1546... Generator Loss: 0.7533
Epoch 1/1... Discriminator Loss: 0.9999... Generator Loss: 0.8961
Epoch 1/1... Discriminator Loss: 0.9848... Generator Loss: 0.9458
Epoch 1/1... Discriminator Loss: 0.7011... Generator Loss: 1.4896
Epoch 1/1... Discriminator Loss: 1.3022... Generator Loss: 0.5589
Epoch 1/1... Discriminator Loss: 1.2266... Generator Loss: 0.6754
Epoch 1/1... Discriminator Loss: 0.8275... Generator Loss: 1.5410
Epoch 1/1... Discriminator Loss: 1.0762... Generator Loss: 0.7518
Epoch 1/1... Discriminator Loss: 0.7763... Generator Loss: 1.8579
Epoch 1/1... Discriminator Loss: 0.9669... Generator Loss: 1.0419
Epoch 1/1... Discriminator Loss: 1.2016... Generator Loss: 0.6836
Epoch 1/1... Discriminator Loss: 0.8620... Generator Loss: 1.3060
Epoch 1/1... Discriminator Loss: 1.0006... Generator Loss: 0.8310
Epoch 1/1... Discriminator Loss: 1.1918... Generator Loss: 0.6588
Epoch 1/1... Discriminator Loss: 1.2825... Generator Loss: 0.5661
Epoch 1/1... Discriminator Loss: 1.0925... Generator Loss: 0.7031
Epoch 1/1... Discriminator Loss: 1.3077... Generator Loss: 0.6232
Epoch 1/1... Discriminator Loss: 0.9079... Generator Loss: 1.4625
Epoch 1/1... Discriminator Loss: 1.4220... Generator Loss: 0.5071
Epoch 1/1... Discriminator Loss: 0.8648... Generator Loss: 1.2234
Epoch 1/1... Discriminator Loss: 0.9609... Generator Loss: 0.9201
Epoch 1/1... Discriminator Loss: 1.0033... Generator Loss: 2.1617
Epoch 1/1... Discriminator Loss: 1.4099... Generator Loss: 0.5035
Epoch 1/1... Discriminator Loss: 0.9976... Generator Loss: 0.9112
Epoch 1/1... Discriminator Loss: 1.2049... Generator Loss: 0.6385
Epoch 1/1... Discriminator Loss: 0.8194... Generator Loss: 1.7525
Epoch 1/1... Discriminator Loss: 1.3812... Generator Loss: 0.5013
Epoch 1/1... Discriminator Loss: 0.9215... Generator Loss: 1.0345
Epoch 1/1... Discriminator Loss: 0.9965... Generator Loss: 0.9526
Epoch 1/1... Discriminator Loss: 1.1959... Generator Loss: 0.6475
Epoch 1/1... Discriminator Loss: 0.6648... Generator Loss: 1.4798
Epoch 1/1... Discriminator Loss: 1.0226... Generator Loss: 0.8157
Epoch 1/1... Discriminator Loss: 1.0345... Generator Loss: 0.8081
Epoch 1/1... Discriminator Loss: 1.1073... Generator Loss: 1.6574
Epoch 1/1... Discriminator Loss: 0.9986... Generator Loss: 1.1197
Epoch 1/1... Discriminator Loss: 0.8999... Generator Loss: 1.3978
Epoch 1/1... Discriminator Loss: 0.9239... Generator Loss: 1.3002
Epoch 1/1... Discriminator Loss: 1.1384... Generator Loss: 0.7096
Epoch 1/1... Discriminator Loss: 0.8272... Generator Loss: 1.2124
Epoch 1/1... Discriminator Loss: 1.0203... Generator Loss: 0.9217

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.